Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning

Paul Van Eecke, Katrien Beuls

Research output: Working paperPreprint

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In this paper, we formulate the challenge of re-conceptualising the language game experimental paradigm in the framework of multi-agent reinforcement learning (MARL). If successful, future language game experiments will benefit from the rapid and promising methodological advances in the MARL community, while future MARL experiments on learning emergent communication will benefit from the insights and results gained from language game experiments. We strongly believe that this cross-pollination has the potential to lead to major breakthroughs in the modelling of how human-like languages can emerge and evolve in multi-agent systems.
Original languageEnglish
Publication statusPublished - 9 Apr 2020


  • cs.AI
  • cs.CL
  • cs.LG
  • cs.MA

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